Workspace Recommendations Based on Prior User Ratings and Similar Selections

ABSTRACT

Techniques are provided for generating workspace recommendations based on prior user ratings of selected workspaces, as well as other similar selections. One method comprises obtaining user workspace ratings provided by a user; calculating a first workspace recommendation score for workspaces that the user previously rated based on the obtained user workspaces ratings; calculating a second workspace recommendation score for additional workspaces that are: (i) similar to workspaces previously rated by the user based on a predefined workspace similarity metric, and/or (ii) selected by similar users, based on a predefined user similarity metric; and recommending workspaces for the user based on the first workspace recommendation score and the second workspace recommendation score. The first workspace recommendation score is further based on a number of times the user selected a given workspace, an average number of times the user selected different workspaces, and/or an average score that the user previously assigned the given workspace.

FIELD

The field relates generally to information processing, and moreparticularly, to workspace allocation techniques.

BACKGROUND

Companies are often looking for new ways to improve their operations.Many companies reduce costs, for example, by encouraging workers to workfully or partially from home, often significantly reducing real estatecosts. This trend is expected to continue to rise. In such a workspacewhere workers do not come to the office on a regular basis, there is aneed for an efficient solution to allow workers to reserve a suitableworkspace for a period of time, such as a given day or a portion of aday.

SUMMARY

In one embodiment, a method comprises obtaining one or more userworkspace ratings that a user previously provided for one or moreworkspaces; calculating a first workspace recommendation score for atleast some of the workspaces that the user previously rated based on theobtained one or more user workspaces ratings; calculating at least asecond workspace recommendation score for one or more additionalworkspaces that are one or more of: (i) similar to workspaces previouslyrated by the user, based on a predefined workspace similarity metric,and (ii) selected by one or more similar users, based on a predefineduser similarity metric; and recommending one or more workspaces for theuser based at least in part on the first workspace recommendation scoreand the second workspace recommendation score.

In some embodiments, the first workspace recommendation score for atleast some of the workspaces that the user previously rated is furtherbased on one or more of a number of times the user selected a givenworkspace, an average number of times the user selected differentworkspaces, and an average score that the user previously assigned thegiven workspace. The second workspace recommendation score for the oneor more additional workspaces that are similar to workspaces previouslyrated by the user may evaluate workspaces having a first workspacerecommendation score above a predefined threshold and identify the oneor more additional workspaces using the predefined workspace similaritymetric. The second workspace recommendation score for the one or moreadditional workspaces selected by the one or more similar users, basedon the predefined user similarity metric, may be identified using one ormore collaborative filters.

Other illustrative embodiments include, without limitation, apparatus,systems, methods and computer program products comprisingprocessor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a workspace recommendation system that generates aweighted workspace scoring for a given user, according to at least oneembodiment;

FIG. 2 illustrates the workspace recommendation system of FIG. 1 infurther detail, according to some embodiments of the disclosure;

FIG. 3 illustrates an exemplary user ratings table, according to atleast one embodiment;

FIG. 4 illustrates an exemplary implementation of the workspaces ofsimilar user recommender of FIG. 2 as a collaborative filter, accordingto an embodiment of the disclosure;

FIG. 5 is a flow chart illustrating an exemplary implementation of aworkspace recommendation process, according to one embodiment of thedisclosure;

FIG. 6 is a flow chart illustrating an exemplary implementation of aworkspace recommendation process, according to one alternate embodimentof the disclosure;

FIG. 7 illustrates an exemplary processing platform that may be used toimplement at least a portion of one or more embodiments of thedisclosure comprising a cloud infrastructure; and

FIG. 8 illustrates another exemplary processing platform that may beused to implement at least a portion of one or more embodiments of thedisclosure.

DETAILED DESCRIPTION

Illustrative embodiments of the present disclosure will be describedherein with reference to exemplary communication, storage and processingdevices. It is to be appreciated, however, that the disclosure is notrestricted to use with the particular illustrative configurations shown.One or more embodiments of the disclosure provide methods, apparatus andcomputer program products for generating workspace recommendations basedon prior user ratings of selected workspaces, as well as other similarselections.

One or more embodiments of the present disclosure provide efficientworkspace recommender systems and methods to allow workers to reserve asuitable workspace for any given day or portion of a day. In someembodiments, the disclosed solution balances constraints and userpreferences to preserve productivity and satisfaction.

In some embodiments, a machine learning-based approach is provided forautomatically recommending seating, based on personal preferences ofworkers, such as a particular building, direction, floor, department andmore. The disclosed workspace recommendation solution will improve overtime based on user feedback and an exploration-exploitation mechanism topreserve a high user satisfaction.

Among other benefits, the disclosed workspace recommendation system canprovide significant cost savings and improve the experience andproductivity of employees and other users of a workspace.

Companies are often looking for new ways to improve their operations.One way to improve operations, for example, is to encourage employees towork from home and save on the costs of enterprise facilities, inaddition to sparing the commute time for the employee. One particularenterprise company estimates a savings of approximately $12M per year inreal estate costs by encouraging employees to work from home, and hasestablished a goal of having 50% of its employees and other workersworking from home at least a few days a week by 2020.

This new enterprise approach leads to a more dynamic work place, withworkspaces dynamically assigned to employees, as needed. In somecompanies, there are no assigned seats at all. Instead, employees andother workers must reserve a workspace every day (e.g., a desk, office,quiet pod or meeting room), based on the type of work that the workerneeds to do that day. This approach has been referred to as hoteling or“beach toweling,” which is becoming more common in the modern businessworld.

It is well known that seating arrangement holds a large impact on theproductivity of employees. For example, mixing employees from differentdepartments and role titles in a given workspace has been shown to leadto higher innovation. Yet managing the workspace pool and personalizingthe workspace pool to needs of each employee is a challenging andtime-consuming task, especially for large organizations.

One or more embodiments of the present disclosure provide a machinelearning-based approach for automatically assigning workers toworkspaces (e.g., specific seats, work rooms, conference rooms and/orflexible workspaces) based on the personal preferences of each worker,as well as an employer strategy, for example, as embodied in one or moreworkspace assignment policies.

In some embodiments, in a preliminary stage, workspaces are suggestedfor a given user based on a questionnaire completed by each employee tospecify one or more preferred features of their desired work location,such as building, direction, floor, department and more. Over time, theprovided suggestions are improved based on the user-providedsatisfaction scores after each workspace assignment. Available workspaceoptions are optionally presented to the employee in a sorted manneraccording to his/her past scores.

In order to provide a representative set of example workspaces, as wellas to incorporate a company strategy, the list of workspace suggestionsprovided in one or more embodiments includes options not yet experiencedby a given user, in some embodiments, in order to increase anexploitation vs. exploration tradeoff for workspace assignments.

Companies are seeking to acquire a talented workforce. With priorapproaches, employees were often required to swipe their employee cardwhen they arrive at a company site for a work day and when they leave atthe end of a work day. This approach made it difficult, if notimpossible, for potential employees with a large commute time and/orlimited availability (e.g., working parents and freelancers) to beeffective and to fully realize their potential. Over time, the life ofworking professionals is becoming more dynamic and increasing vehiculartraffic is adding to the commute time in many regions. This increasesthe need for a new approach to allow for remote connections and anavailability that is not necessarily physical. This trend is beingadopted by an increasing number of organizations around the world.

In a dynamic workplace, where workers are dynamically assignedworkspaces as needed, one naive approach is to assign employees withrandom seats. It can be shown, however, that personalized workenvironments that are specifically assigned to employees, based on theirpreferences, can have a significant affect both on the employee level ofsatisfaction as well on employee productivity. For example, assigning anemployee whose job requires meticulous concentration with a noisylocation in a crowded open space will have a devastating effect on hisor her efficiency and productivity. On the other hand, if a companysuffers from a lack of innovation and wants to push its employees toextract their creativity, mixing different role jobs and departments canbe a good idea. In many cases, taking the preferences of employees intoconsideration aligns and enhances company strategy.

With the improvements in available network speed, teleconferencing andthe beginning of virtual reality applications, arriving and utilizing acommon physical workspace on a daily basis is simply wasteful. Some ofthe immediate costs associated with the daily presence and use offacilities by employees are real estate costs, network and connectivitycosts, electricity and other utility costs, insurance costs, and/or foodand beverage costs (e.g., tea and coffee). An employee that works fromhome for at least a portion of the week “saves” the company workinghours lost on commute time, planned breaks and unplanned “corridorencounters.”

A manual assignment of workspaces to employees can be a meticulous andtime-consuming task, as it needs to take multiple limitations intoaccount. Workspace availability, location, and surrounding role titlesare only some of these limitations. At large company sites, for example,where the number of employees can reach thousands, this daily task canheavily consume corporate resources and become virtually impossible whenapproached manually (thus, some level of automation is needed).

FIG. 1 illustrates a workspace recommendation system 100 that generatesa weighted workspace scoring for a given user, according to at least oneembodiment. As shown in FIG. 1, workspace ratings 110 from multipleusers, including the given user, are applied to the workspacerecommendation system 100. The exemplary workspace recommendation system100 generates workspace recommendations 150 based on prior workspacesrating 110 of selected workspaces by the given user, as well as othersimilar selections, as discussed further below.

FIG. 2 illustrates the workspace recommendation system 100 of FIG. 1 infurther detail, according to some embodiments of the disclosure. In theexample of FIG. 2, workspace ratings 210 from the given user, workspaceratings 220 from other users, and attributes of the workspaces 230 areapplied to a workspace recommender 240 that generates workspacerecommendations 280.

The exemplary workspace recommender 240 comprises a user ratingrecommender 250, a similar workspace recommender 260 and a workspaces ofsimilar users recommender 270.

In one or more embodiments, the user rating recommender 250 generatestailored workspace recommendations for a given user based on workspacesthat were rated by the given user in the past. In one embodiment, aweighted workspace score is determined by the user rating recommender250, as follows:

WS _(i)=(v _(i)/(v _(i) +m))×R _(i)+(m/(v _(i) +m))×C   (1)

where WS_(i) is the weighted score of workspace i, v_(i) is the numberof times that the given user voted for workspaces i, m is the averagenumber of votes the given user placed for different workspaces so far,R_(i) is the average score that the given user gave workspaces i in thepast and C is the average score over all past votes of the given user.In this manner, equation (1) takes into account the number of times theuser favored the same workspace.

In the example of FIG. 2, the exemplary similar workspace recommender260 generates tailored workspace recommendations for the given userusing one or more additional workspaces that are similar to workspacespreviously rated by the given user, based on a predefined workspacesimilarity metric (e.g., workspaces that were not yet rated by the givenuser but have a high similarity to previously chosen workspaces).

In one exemplary implementation, the similar workspace recommender 260processes workspaces previously rated by the given user that have aweighted score according to equation (1) above a predefined threshold.The similar workspace recommender 260 then identifies additionalworkspaces that are similar to the identified previously ratedworkspaces using a predefined workspace similarity metric, such as aJaccard similarity score on the set of additional workspaces and theircorresponding workspace attributes 230, such as building, direction,floor and department.

For example, the exemplary similar workspace recommender 260 candetermine a similar workspace weighted score, as follows:

WS _(j) =S _(i,j) ×WS _(i)   (2)

where WS_(j) is the similar workspace, and S_(i,j) is the similarityscore between the workspaces. If the same workspace gets severalsimilarity WS_(j) scores, then a maximum similarity WS_(j) score can beselected in at least one embodiment.

In the example of FIG. 2, the exemplary workspaces of similar usersrecommender 270 generates tailored workspace recommendations for thegiven user using one or more additional workspaces that were selected byone or more users that are similar to the given user, based on apredefined user similarity metric (e.g., highly rated seats by similarusers), for example, using user-based collaborative filtering, asdiscussed further below in conjunction with FIG. 4.

User-based collaborative filtering is a known method for generatingrecommendations for a user with items that similar users previouslyliked. The exemplary workspaces of similar users recommender 270 candetermine a workspace of similar users weighted score, as follows:

$\begin{matrix}{{{WS_{i}} = \frac{\Sigma \left( {R_{v,i} \times S_{u,v}} \right)}{s_{u,v}}},} & (3)\end{matrix}$

where R_(v,i) is the rating given by a user v to the workspace i, andS_(u,v) is the similarity score between users. Similarity of users canbe calculated, for example, using a predefined user similarity metric,such as a Pearson correlation. In one exemplary implementation, therecommendations can be limited to those users with a predefined usersimilarity metric greater than a predefined threshold.

A final workspace recommendation score for a given workspace, in theevent that a weighted score that comes from more than one of theworkspace recommender 240, user rating recommender 250 and similarworkspace recommender 260, can be selected, for example, as a maximum ofthe available workspace recommendation scores.

FIG. 3 illustrates an exemplary user ratings table 300, according to atleast one embodiment. Generally, the exemplary user ratings table 300aggregates the workspace ratings 210 from the given user and theworkspace ratings 220 from other users in a single table. As shown inFIG. 3, each column of the exemplary user ratings table 300 correspondsto a different workspace, and each row of the exemplary user ratingstable 300 corresponds to a different user. Thus, a given element of theexemplary user ratings table 300 indicates the user rating of a givenworkspace by a given user. It is noted that row a in FIG. 3 correspondsto a given active user 310 for which a workspace recommendation is beinggenerated and column j of the user ratings table 300 corresponds to agiven workspace 320 for which a rating is being sought.

FIG. 4 illustrates an exemplary implementation 400 of the workspaces ofsimilar users recommender 270 of FIG. 2 as a collaborative filter 450,according to an embodiment of the disclosure. As shown in FIG. 4, theuser ratings table 300 of FIG. 3 is applied to the collaborative filter450, which generates a prediction 460 on workspace j for the givenactive user, and optionally, a top N list of available workspaces 470for the given active user. In some embodiments, the generated top N listof available workspaces 470 is the based on the past ratings of thegiven active user and similar users preferences.

The exemplary collaborative filter 450 may be implemented, for example,using the techniques described in Prem Melville and Vikas Sindhwani,“Recommender Systems,” in Encyclopedia of Machine Learning (Springer,Claude Sammut and Geoffrey Webb (Eds), 2010), incorporated by referenceherein in its entirety.

One or more embodiments of the disclosed techniques for generatingworkspace recommendations based on prior user ratings of selectedworkspaces, as well as other similar selections, automatically provide agiven user with a list of available workspaces ordered by the likelihoodthat the given user will give them a high rating.

In one exemplary implementation, an enterprise can employ the followingsteps to apply the disclosed solution for generating workspacerecommendations for a given user based on prior user ratings of selectedworkspaces by the given user, as well as other similar selections:

above-described weighted scores can be calculated every day, forexample, at night;

obtain a list of available workspaces;

generate ordered list of available workspaces, ranked by the weightedscores generated by the workspace recommendation system 100 of FIG. 2;

present given user with a top N list of workspaces; and

for exploration, e.g., suggesting options outside the regularpreferences of the given user, an exploration coefficent can be chosenby the given user. For example, for a top-5 list, a rate of 1:4exploration vs. exploitation can be employed. In this case, oneexploration workspace can be selected randomly or according to apredefined strategy that the company is seeking to promote, and replacethe workspace having the lowest weighed score in the top-5 list seatwith the one exploration workspace.

FIG. 5 is a flow chart illustrating an exemplary implementation of aworkspace recommendation process 500, according to one embodiment of thedisclosure. As shown in FIG. 5, the exemplary workspace recommendationprocess 500 initially obtains one or more user workspace ratings that auser previously provided for one or more workspaces during step 510.Thereafter, a first workspace recommendation score for at least some ofthe workspaces that the user previously rated is calculated during step520, based on the obtained one or more user workspaces ratings.

The exemplary workspace recommendation process 500 then calculates asecond workspace recommendation score during step 530 for one or moreadditional workspaces that are: (i) similar to workspaces previouslyrated by the user, based on a predefined workspace similarity metric,and/or (ii) selected by one or more similar users, based on a predefineduser similarity metric.

Finally, the exemplary workspace recommendation process 500 recommendsone or more workspaces for the user during step 540 based on the firstworkspace recommendation score and/or the second workspacerecommendation score.

FIG. 6 is a flow chart illustrating an exemplary implementation of aworkspace recommendation process 600, according to one alternateembodiment of the disclosure. As shown in FIG. 6, the exemplaryworkspace recommendation process 600 initially obtains a set of weightedscores, S₁ through S_(n), assigned to corresponding workspaces duringstep 610. The workspace recommendation process 600 then uses theweighted scores, S, during step 620 to order the list of workspaces, asshown in step 630.

A top N list of workspaces is presented to the user during step 650. Inaddition, randomness can optionally be added to the top N list ofworkspaces during step 640, according to user preferences. For example,as indicated above, for a top-5 list, a rate of 1:4 of exploration vs.exploitation can be employed. In this case, one exploration workspacecan be selected randomly or according to a predefined strategy that thecompany is seeking to promote during step 640, and the workspace havingthe lowest weighed score in the top-5 list seat can be replaced with theone exploration workspace.

Among other benefits, the disclosed workspace recommendation techniquesallow employers to permit workers to work where they want, which helpsin recruiting the best talent from everywhere in the country (if not theworld). For example, generation Y workers are known to be extremelysmart and technology-savvy. Such generation Y workers are oftencontinuously plugged in through their smartphones, laptops and otherdevices and can basically perform their jobs anywhere. This generationalso likes to job-hop and they are often on the lookout for the mostappealing opportunities, preferably at a company that allows mostself-fulfillment and maximum flexibility at the same time. Companiesaiming to appeal to this profile of workers need to have updatedpolicies regarding work location. Some of these new work forms is“hoteling” whereby employees must reserve a workspace every day, and“beach toweling” whereby employees do not even reserve a workspace butinstead must claim the space they want as they come in.

One or more aspects of the present disclosure recognize that differentworkers have different preferences for an ideal working environment thatallow them to reach their full potential and to become more productive.To achieve this goal, workspace assignment must be personalizedaccording to employee declarations based on past experiences of good andbad locations. By learning and predicting individual preferences andassigning workspaces accordingly, workspace arrangements can alsoreflect the company strategy. For example, if one company goal is tocreate more collaboration across departments, some of the workspacesuggestions will refer workers to locations removed from their immediateteammates and colleagues.

As noted above, financially, the business case for not having a physicaloffice that supports an entire workforce is clear, resulting in lessoverhead. In addition, the cost savings provided by the disclosedtechniques for generating workspace recommendations based on prior userratings of selected workspaces, as well as other similar selections,also extend to the workers who save significant money each year oncommuting costs.

In one or more embodiments, the disclosed techniques for generatingworkspace recommendations based on prior user ratings of selectedworkspaces, as well as other similar selections, employ machine learningalgorithms on historical ratings of employees for workspaces theyexperienced, instead of coming up with suggestions manually (which cancreate huge overhead at scale). The disclosed solution suggests the bestoptions available with regard to different workspace attributes (e.g.,location, department, open space vs. office space and more). The systeminitially learns the individual employee preferences, at first based ona questionnaire the employee answers in some embodiments, and later byworker ratings of the provided suggestions. Over time, the predictionsbecome more and more accurate. Once the learning phase is over, thedisclosed workspace recommendation solution can integrate suggestionsbased on the learned behavior (e.g., exploitation) along withsuggestions aligned with a company strategy (e.g., exploration). Thisallows the employee to experience workspaces that the employee would notnormally choose.

One or more embodiments of the disclosure provide improved methods,apparatus and computer program products for for generating workspacerecommendations based on prior user ratings of selected workspaces, aswell as other similar selections. The foregoing applications andassociated embodiments should be considered as illustrative only, andnumerous other embodiments can be configured using the techniquesdisclosed herein, in a wide variety of different applications.

It should also be understood that the disclosed workspace recommendationtechniques, as described herein, can be implemented at least in part inthe form of one or more software programs stored in memory and executedby a processor of a processing device such as a computer. As mentionedpreviously, a memory or other storage device having such program codeembodied therein is an example of what is more generally referred toherein as a “computer program product.”

The disclosed techniques for generating workspace recommendations basedon prior user ratings of selected workspaces, as well as other similarselections may be implemented using one or more processing platforms.One or more of the processing modules or other components may thereforeeach run on a computer, storage device or other processing platformelement. A given such element may be viewed as an example of what ismore generally referred to herein as a “processing device.”

As noted above, illustrative embodiments disclosed herein can provide anumber of significant advantages relative to conventional arrangements.It is to be appreciated that the particular advantages described aboveand elsewhere herein are associated with particular illustrativeembodiments and need not be present in other embodiments. Also, theparticular types of information processing system features andfunctionality as illustrated and described herein are exemplary only,and numerous other arrangements may be used in other embodiments.

In these and other embodiments, compute services can be offered to cloudinfrastructure tenants or other system users as a Platform-as-a-Service(PaaS) offering, although numerous alternative arrangements arepossible.

Some illustrative embodiments of a processing platform that may be usedto implement at least a portion of an information processing systemcomprise cloud infrastructure including virtual machines implementedusing a hypervisor that runs on physical infrastructure. The cloudinfrastructure further comprises sets of applications running onrespective ones of the virtual machines under the control of thehypervisor. It is also possible to use multiple hypervisors eachproviding a set of virtual machines using at least one underlyingphysical machine. Different sets of virtual machines provided by one ormore hypervisors may be utilized in configuring multiple instances ofvarious components of the system.

These and other types of cloud infrastructure can be used to providewhat is also referred to herein as a multi-tenant environment. One ormore system components such as a cloud-based workspace recommenderengine, or portions thereof, are illustratively implemented for use bytenants of such a multi-tenant environment.

Cloud infrastructure as disclosed herein can include cloud-based systemssuch as Amazon Web Services (AWS), Google Cloud Platform (GCP) andMicrosoft Azure. Virtual machines provided in such systems can be usedto implement at least portions of a cloud-based workspace recommenderplatform in illustrative embodiments. The cloud-based systems caninclude object stores such as Amazon S3, GCP Cloud Storage, andMicrosoft Azure Blob Storage.

In some embodiments, the cloud infrastructure additionally oralternatively comprises a plurality of containers implemented usingcontainer host devices. For example, a given container of cloudinfrastructure illustratively comprises a Docker container or other typeof Linux Container (LXC). The containers may run on virtual machines ina multi-tenant environment, although other arrangements are possible.The containers may be utilized to implement a variety of different typesof functionality within the storage devices. For example, containers canbe used to implement respective processing devices providing computeservices of a cloud-based system. Again, containers may be used incombination with other virtualization infrastructure such as virtualmachines implemented using a hypervisor.

Illustrative embodiments of processing platforms will now be describedin greater detail with reference to FIGS. 7 and 8. These platforms mayalso be used to implement at least portions of other informationprocessing systems in other embodiments.

FIG. 7 shows an example processing platform comprising cloudinfrastructure 700. The cloud infrastructure 700 comprises a combinationof physical and virtual processing resources that may be utilized toimplement at least a portion of the workspace recommendation system 100of FIGS. 1 and 2. The cloud infrastructure 700 comprises multiplevirtual machines (VMs) and/or container sets 702-1, 702-2, . . . 702-Limplemented using virtualization infrastructure 704. The virtualizationinfrastructure 704 runs on physical infrastructure 705, andillustratively comprises one or more hypervisors and/or operating systemlevel virtualization infrastructure. The operating system levelvirtualization infrastructure illustratively comprises kernel controlgroups of a Linux operating system or other type of operating system.

The cloud infrastructure 700 further comprises sets of applications710-1, 710-2, . . . 710-L running on respective ones of theVMs/container sets 702-1, 702-2, . . . 702-L under the control of thevirtualization infrastructure 704. The VMs/container sets 702 maycomprise respective VMs, respective sets of one or more containers, orrespective sets of one or more containers running in VMs.

In some implementations of the FIG. 7 embodiment, the VMs/container sets702 comprise respective VMs implemented using virtualizationinfrastructure 704 that comprises at least one hypervisor. Suchimplementations can provide workspace recommender functionality of thetype described above for one or more processes running on a given one ofthe VMs. For example, each of the VMs can implement workspacerecommender control logic and associated workspace scoring techniquesfor providing workspace recommender functionality for one or moreprocesses running on that particular VM.

An example of a hypervisor platform that may be used to implement ahypervisor within the virtualization infrastructure 704 is the VMware®vSphere® which may have an associated virtual infrastructure managementsystem such as the VMware® vCenter™. The underlying physical machinesmay comprise one or more distributed processing platforms that includeone or more storage systems.

In other implementations of the FIG. 7 embodiment, the VMs/containersets 702 comprise respective containers implemented using virtualizationinfrastructure 704 that provides operating system level virtualizationfunctionality, such as support for Docker containers running on baremetal hosts, or Docker containers running on VMs. The containers areillustratively implemented using respective kernel control groups of theoperating system. Such implementations can provide workspace recommenderfunctionality of the type described above for one or more processesrunning on different ones of the containers. For example, a containerhost device supporting multiple containers of one or more container setscan implement one or more instances of workspace recommender controllogic and associated workspace scoring for use in generating workspacerecommendations.

As is apparent from the above, one or more of the processing modules orother components of system 100 may each run on a computer, server,storage device or other processing platform element. A given suchelement may be viewed as an example of what is more generally referredto herein as a “processing device.” The cloud infrastructure 700 shownin FIG. 7 may represent at least a portion of one processing platform.Another example of such a processing platform is processing platform 800shown in FIG. 8.

The processing platform 800 in this embodiment comprises at least aportion of the given system and includes a plurality of processingdevices, denoted 802-1, 802-2, 802-3, . . . 802-K, which communicatewith one another over a network 804. The network 804 may comprise anytype of network, such as a wireless area network (WAN), a local areanetwork (LAN), a satellite network, a telephone or cable network, acellular network, a wireless network such as WiFi or WiMAX, or variousportions or combinations of these and other types of networks.

The processing device 802-1 in the processing platform 800 comprises aprocessor 810 coupled to a memory 812. The processor 810 may comprise amicroprocessor, a microcontroller, an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or other type ofprocessing circuitry, as well as portions or combinations of suchcircuitry elements, and the memory 812, which may be viewed as anexample of a “processor-readable storage media” storing executableprogram code of one or more software programs.

Articles of manufacture comprising such processor-readable storage mediaare considered illustrative embodiments. A given such article ofmanufacture may comprise, for example, a storage array, a storage diskor an integrated circuit containing RAM, ROM or other electronic memory,or any of a wide variety of other types of computer program products.The term “article of manufacture” as used herein should be understood toexclude transitory, propagating signals. Numerous other types ofcomputer program products comprising processor-readable storage mediacan be used.

Also included in the processing device 802-1 is network interfacecircuitry 814, which is used to interface the processing device with thenetwork 804 and other system components, and may comprise conventionaltransceivers. The other processing devices 802 of the processingplatform 800 are assumed to be configured in a manner similar to thatshown for processing device 802-1 in the figure.

Again, the particular processing platform 800 shown in the figure ispresented by way of example only, and the given system may includeadditional or alternative processing platforms, as well as numerousdistinct processing platforms in any combination, with each suchplatform comprising one or more computers, storage devices or otherprocessing devices.

Multiple elements of an information processing system may becollectively implemented on a common processing platform of the typeshown in FIG. 7 or 8, or each such element may be implemented on aseparate processing platform.

For example, other processing platforms used to implement illustrativeembodiments can comprise different types of virtualizationinfrastructure, in place of or in addition to virtualizationinfrastructure comprising virtual machines. Such virtualizationinfrastructure illustratively includes container-based virtualizationinfrastructure configured to provide Docker containers or other types ofLXCs.

As another example, portions of a given processing platform in someembodiments can comprise converged infrastructure such as VxRail™,VxRack™, VxBlock™, or Vblock® converged infrastructure commerciallyavailable from Dell EMC.

It should therefore be understood that in other embodiments differentarrangements of additional or alternative elements may be used. At leasta subset of these elements may be collectively implemented on a commonprocessing platform, or each such element may be implemented on aseparate processing platform.

Also, numerous other arrangements of computers, servers, storage devicesor other components are possible in the information processing system.Such components can communicate with other elements of the informationprocessing system over any type of network or other communication media.

As indicated previously, components of an information processing systemas disclosed herein can be implemented at least in part in the form ofone or more software programs stored in memory and executed by aprocessor of a processing device. For example, at least portions of thefunctionality shown in one or more of the figures are illustrativelyimplemented in the form of software running on one or more processingdevices.

It should again be emphasized that the above-described embodiments arepresented for purposes of illustration only. Many variations and otheralternative embodiments may be used.

For example, the disclosed techniques are applicable to a wide varietyof other types of information processing systems. Also, the particularconfigurations of system and device elements and associated processingoperations illustratively shown in the drawings can be varied in otherembodiments. Moreover, the various assumptions made above in the courseof describing the illustrative embodiments should also be viewed asexemplary rather than as requirements or limitations of the disclosure.Numerous other alternative embodiments within the scope of the appendedclaims will be readily apparent to those skilled in the art.

What is claimed is:
 1. A method, comprising: obtaining one or more userworkspace ratings that a user previously provided for one or moreworkspaces; calculating a first workspace recommendation score for atleast some of the workspaces that the user previously rated based on theobtained one or more user workspaces ratings; calculating at least asecond workspace recommendation score for one or more additionalworkspaces that are one or more of: (i) similar to workspaces previouslyrated by the user, based on a predefined workspace similarity metric,and (ii) selected by one or more similar users, based on a predefineduser similarity metric; and recommending one or more workspaces for theuser based at least in part on the first workspace recommendation scoreand the second workspace recommendation score, wherein the method isperformed by at least one processing device comprising a processorcoupled to a memory.
 2. The method of claim 1, wherein the one or moreworkspaces comprise one or more of a seat, a desk, a table and a room.3. The method of claim 1, wherein the first workspace recommendationscore for at least some of the workspaces that the user previously ratedis further based on one or more of a number of times the user selected agiven workspace, an average number of times the user selected differentworkspaces, and an average score that the user previously assigned thegiven workspace.
 4. The method of claim 1, wherein the second workspacerecommendation score for the one or more additional workspaces that aresimilar to workspaces previously rated by the user evaluates workspaceshaving a first workspace recommendation score above a predefinedthreshold and identifies the one or more additional workspaces using thepredefined workspace similarity metric.
 5. The method of claim 4,wherein the predefined workspace similarity metric comprises a Jaccardsimilarity using one or more attributes of the workspaces.
 6. The methodof claim 1, wherein the second workspace recommendation score for theone or more additional workspaces selected by the one or more similarusers, based on the predefined user similarity metric, are identifiedusing one or more collaborative filters.
 7. The method of claim 1,wherein the predefined user similarity metric employs a Pearsoncorrelation.
 8. The method of claim 1, wherein the recommending one ormore workspaces for the user comprises providing a list of availableworkspaces sorted based on one or more of the first workspacerecommendation score and the second workspace recommendation score. 9.The method of claim 1, wherein the first workspace recommendation scoreexploits workspaces previously rated by a given user and the secondworkspace recommendation score explores different workspaces notpreviously rated by the given user.
 10. A computer program product,comprising a tangible machine-readable storage medium having encodedtherein executable code of one or more software programs, wherein theone or more software programs when executed by at least one processingdevice perform the following steps: obtaining one or more user workspaceratings that a user previously provided for one or more workspaces;calculating a first workspace recommendation score for at least some ofthe workspaces that the user previously rated based on the obtained oneor more user workspaces ratings; calculating at least a second workspacerecommendation score for one or more additional workspaces that are oneor more of: (i) similar to workspaces previously rated by the user,based on a predefined workspace similarity metric, and (ii) selected byone or more similar users, based on a predefined user similarity metric;and recommending one or more workspaces for the user based at least inpart on the first workspace recommendation score and the secondworkspace recommendation score.
 11. The computer program product ofclaim 10, wherein the second workspace recommendation score for the oneor more additional workspaces that are similar to workspaces previouslyrated by the user evaluates workspaces having a first workspacerecommendation score above a predefined threshold and identifies the oneor more additional workspaces using the predefined workspace similaritymetric.
 12. The computer program product of claim 10, wherein the secondworkspace recommendation score for the one or more additional workspacesselected by the one or more similar users, based on the predefined usersimilarity metric, are identified using one or more collaborativefilters.
 13. The computer program product of claim 10, wherein thepredefined user similarity metric employs a Pearson correlation.
 14. Thecomputer program product of claim 10, wherein the first workspacerecommendation score exploits workspaces previously rated by a givenuser and the second workspace recommendation score explores differentworkspaces not previously rated by the given user.
 15. An apparatus,comprising: a memory; and at least one processing device, coupled to thememory, operative to implement the following steps: obtaining one ormore user workspace ratings that a user previously provided for one ormore workspaces; calculating a first workspace recommendation score forat least some of the workspaces that the user previously rated based onthe obtained one or more user workspaces ratings; calculating at least asecond workspace recommendation score for one or more additionalworkspaces that are one or more of: (i) similar to workspaces previouslyrated by the user, based on a predefined workspace similarity metric,and (ii) selected by one or more similar users, based on a predefineduser similarity metric; and recommending one or more workspaces for theuser based at least in part on the first workspace recommendation scoreand the second workspace recommendation score.
 16. The apparatus ofclaim 15, wherein the second workspace recommendation score for the oneor more additional workspaces that are similar to workspaces previouslyrated by the user, evaluates workspaces having a first workspacerecommendation score above a predefined threshold and identifies the oneor more additional workspaces using the predefined workspace similaritymetric.
 17. The apparatus of claim 15, wherein the second workspacerecommendation score for the one or more additional workspaces selectedby the one or more similar users, based on the predefined usersimilarity metric, are identified using one or more collaborativefilters.
 18. The apparatus of claim 15, wherein the predefined usersimilarity metric employs a Pearson correlation.
 19. The apparatus ofclaim 15, wherein the recommending one or more workspaces for the usercomprises providing a list of available workspaces sorted based on oneor more of the first workspace recommendation score and the secondworkspace recommendation score.
 20. The apparatus of claim 15, whereinthe first workspace recommendation score exploits workspaces previouslyrated by a given user and the second workspace recommendation scoreexplores different workspaces not previously rated by the given user.